Differential Genomic Expression Profiles In Mouse Cumulus Cells During In Vitro Maturation

Background and Objectives:The ovarian follicle as the basic functional unit of the ovary, consists of a single oocyte surrounded by somatic granulosa cells(GCs) and outer layers of theca cells. Folliculogenesis is a complex and carefully regulated process, during which oocyte gradually acquires the developmental competence ready for further fertilization and embryo development. As follicle grows and an antrum is formed, granulosa cells differentiate into two distinct sub-types: ① the mural granulosa cells(MGCs), thoses lining the follicle wall act as a role of steroidogenesis and follicle rupture; ② the cumulus granulosa cells(CCs), the cells surrounding the oocyte and keep an intimate relationship with it making up the specific structure called cumulus oocyte complex(COC). It is well known that the bio-directional communications between oocyte and the surrounding somatic cells occur throughout folliculogenesis, by means of gap junctions and paracrine factors which maintains the optimal micro-environment for oocyte development. In the pre-antral stage, follicular growth is FSH independent and relies on the KITL-KIT system. Briefly, GCs secrete KIT ligand(KITL) acting on its receptor KIT on the surface of oolemma to support the growth of the oocyte, in turn, oocytes produce factors mainly as differentiation factor 9(GDF9) and bone morphogenetic protein 15(BMP15) to promote GCs proliferation. During the antral stage, CCs transfer micromolecules such as c GMP and c AMP to oocyte to maintain meiotic arrest at prophase I and are also regulated by oocyte to keep its phenotype distinct from MGCs. As to the ovulation stage, oocyte and CCs coordinated work together to complete the final cumulus expansion and resumption of meiosis. Additionally, as to the metabolism, CCs compensate the oocyte-deficient metabolic moleculars such as pyruvate, alanine and cholesterol to support the oocyte development and ooctye also promotes the expressions of some metabolic enzymes in CCs. The intimate interactions between oocyte and CCs indicate that some distinctive characteristics must be imprinted in CCs during oocyte maturation, including some specific genomic expressions, molecules and signaling in CCsOocyte in vitro maturation(IVM) is a method refers to isolation of immature COC(GV-COC) from antral follicle to culture to maturation stage(MII-COC) under specific laboratory conditions. As an advanced assisted reproductive technology(ART), IVM can be particularly in use in the patients at high risks of ovarian hyperstimulation syndrome(OHSS) or those with polycystic ovarian syndrome(PCOS). Recently, IVM is also applied to those women with malignant tumor who require rapid fertility preservation before beginning potentially gonadotoxic treatments. Despite of the above advantages of IVM, it is still no widely used in clinic because of the higher miscarriage rates of oocyte compared with conventional in vitro fertilization(IVF). However, the causes of attenuate developmental competence of oocytes undergoing IVM are still remained to be identified. Collectively, it is necessary to explore the specific genes expressed in CCs for our better understanding of the mechanisms of oocyte maturation and further optimize the IVM culture systems. Currently, mouse COC in vitro culture systems and IVM are the widely used modles to study thatAlong with the advent and development of high-throughput field, many technologies such as gene chip and next generation sequence have made it possible to detect the genomic profiles of GCs or CCs. Recently, increasing studies using the microarray and real-time PCR to focus on the gene expressions in granulosa cells or CCs and expect to seek for some specific biomarkers to evaluate the oocyte quality. Meanwhile, studies aiming at screening the changing expression genes in CCs or oocytes under different maturation conditions were also carried out. Despite of the accumulating studies have provided the useful transcriptomics dataset linked to oocyte competence, data regarding the gene expression profiles in CCs associated with the oocyte maturation stage after IVM are limited. Therefore, we hypothesize that based on the noted mutual regulatory loop between oocyte and somatic cells, a vast changes of gene expression profiles of CCs must occur during IVM, which could be detected by high-through technologies, furthermore, among which some specific functional genes may be determined to indirectly evaluate the oocyte developmental competence.To prove the above hypothesis and to further understand the potential mechanisms of oocyte maturation. We studied the genomic expression profiles of CCs using high-through technologies based on mouse IVM and IVF models, so as to determine some specific bio-markers in CCs to evaluate oocyte nuclear and cytoplasmic developmental competence. As one part of our studies, we established the gene expression profiles of CCs before and after IVM via microarray hybridization. Meanwhile, we selected some functional genes among the differentially expressed genes and further studied the quantifications and cellular localizations both in m RNA and protein levels.Material and Methods:⑴ Establishment of the mouse IVF model: GV-COCs were recovered from 3-week-old ICR female mice underwent intraperitoneal injection of 5U PMSG superovulated treatment and cultured to maturation(MII-COCs). The culture system was reference to the latest literatures.⑵ Obtain of the differential expression profiles: We stripped the CCs from GV-COCs and in vitro matured MII-COCs, defined as GV-CCs and MII-CCsrespectively. Each group contains 3 independent samples(100COCs were mixed as one sample). Total RNA was extracted and used to hybridize to the microarray chip to get the differential gene expression profiles.⑶ Bioinformatic analysis of microarray results: Determin some specific target genes among the differentially expression profiles and the differentially enriched biological events and functions.⑷ Validation of the microarray results : We selected some candidate genes from the differentially expression genes based on the bioinformatic analysis results as well as the literature reports. The RNA derived from the same samples as microarray was used to validate the results of microarray by q PCR.⑸ Biological significance of the microarray results: After the above preliminary validation, we finally focused on some specific functional genes and q PCR was carried out with another extensive samples, 8 samples in each group(30COCs were mixed as one sample).⑹ Validation of the functional genes in protein levels: We striped CCs from CV-COCs and MII-COCs respectively, and performed western blot and immunofluorescence to study the quantifications and cellular localizations of the above functional genes.⑺ Statistical methods: Original data were statistically analysised by SPSS 20.0. The rates of maturation(MII%) were analysised by Chi-squared test. The m RNA and protein differences of candidate genes between GV-CCs and MII-CCs groups were evaluated by independent-sample t-test and data were presented as mean±SD. P<0.05 was set as cut point and defined to be statistical significance.Results:⑴ The stable model of mouse IVM has been established successfully and through which we could obtain the GV-COCs and MII-COCs, the average rate of maturation(ie. MII%) equals 92.6%(p>0.05).⑵ Differential expression profiles in CCs before and after IVM have been obtained after microarray hybridization. After setting of fold change cut-off≥3.0 and P value cut-off ≤0.05, we obtained: ①2615 up-regulated genes mainly involved in metalloenzyme regulator activity and EGFR binding. ②2808 down-regulated genes mainly related to endonuclear related biological functions,when MII-CCs compared with GV-CCs.⑶ Eleven genes referring 4 biological events, those up-regulated are ECM & EGF related genes(PTGS2, EREG, TNFAIP6,EFEMP1), mitochondrial metabolism related genes(FDX1, AIFM2); down-regulated are gap junctions and cell cycle related genes(GJA1, GJA4, CCND2,CCNA2,CCNB2) were validated by q PCR using the same samples as microarray and revealed the same expression pattern as microarray.⑷ Genes include EFEMP1,FDX1,GJA1 and CCND2 were picked out as our functional genes and further validated via q PCR using another 8 samples and the results were totally in accordance with the preliminary validation as well as the microarray chip.⑸ Protein levels of EFEMP1,FDX1,GJA1 and CCND2 were in line with the m RNA levels. ①EFEMP1 was mainly localized in the surface layer of CCs before IVM. Interestingly, after IVM, expression of EFEMP1 was extended to all of CCs and even in the extracellular matrix between CCs. Meanwhile, EFEMP1 showed high expression in the MII-oocyte while missing expression in GV-oocyte. ②FDX1 was localized in both outer and inner layers of CCs of GV-COCs. In MII-COCs,FDX1 protein was localized in all of CCs but also oocyte. The fluorescent of FDX1 protein was presented mainly within cytoplasm of CCs and oocyte. ③GJA1 was only localized in the cytoplasm in CCs while was not expressed in oocyte. GJA1 positive expression was found in most of CCs in GV-COCs, while the positive stained CCs were significantly decreased after IVM. ④CCND2 was mainly expressed in the inner layer of CCs of GV-COCs. After IVM, this specific pattern was changed with the homogeneous distribution in MII-CCs. Moreover, the fluorescence intensity of CCND2 in MII-CCs was decreased.Conclusion:⑴ Master of the micromanipulation technologies of mouse oocyte and establish the mouse stable IVM model which could be used for further studies.⑵ A vast of genetic changes occurred in CCs during oocyte in vitro maturation process. Genes involved in metalloenzyme regulator activity and EGFR binding were up regulated and genes related to endonuclear biological events were down regulated, which have been validated by q PCR.⑶ After the amplifying validation, some functional genes such as(EFEMP1, FDX1, GJA1, CCND2) were differentially expressed which showed some critical biological significances.⑷ During IVM, some up-regulated factors such as EFEMP1 and FDX1, down-regulated factors such as GJA1 and CCND2 could be used as biomarkers associated with oocyte maturation and developmental competence and eventually be used to optimize the IVM systems.